Quantity versus Diversity: Influence of Data on Detecting EEG Pathology with Advanced ML Models
This work addresses the challenge of data variability in EEG analysis for medical diagnostics, offering insights into model robustness, but it is incremental as it builds on existing methods with new datasets.
The study investigated how data quantity and diversity affect machine learning models for EEG pathology detection, finding that while diverse data can degrade performance, increasing data quantity improves accuracy and can compensate for diversity, with a meta-model combining neural networks and gradient boosting achieving superior results.
This study investigates the impact of quantity and diversity of data on the performance of various machine-learning models for detecting general EEG pathology. We utilized an EEG dataset of 2,993 recordings from Temple University Hospital and a dataset of 55,787 recordings from Elmiko Biosignals sp. z o.o. The latter contains data from 39 hospitals and a diverse patient set with varied conditions. Thus, we introduce the Elmiko dataset - the largest publicly available EEG corpus. Our findings show that small and consistent datasets enable a wide range of models to achieve high accuracy; however, variations in pathological conditions, recording protocols, and labeling standards lead to significant performance degradation. Nonetheless, increasing the number of available recordings improves predictive accuracy and may even compensate for data diversity, particularly in neural networks based on attention mechanism or transformer architecture. A meta-model that combined these networks with a gradient-boosting approach using handcrafted features demonstrated superior performance across varied datasets.